EBLIG Report: Interest Group Meets in Atlantic Canada and New Co-convenors are Chosen
Bibliographic record
Abstract
May was a month of travel for many Evidence-Based Librarianship Interest Group (EBLIG) members. From May 6 – 11, the 4th International Evidence Based Library and Information Practice conference was held in Durham-Chapel Hill, North Carolina. From May 22 – 26, EBLIG members attended the Canadian Library Association (CLA) conference in St. John’s, Newfoundland. As the second anniversary of the formation of EBLIG has come around, the inaugural co-conveners, Lyn Currie and Virginia Wilson, are wrapping up their two-year term. Congratulations to Sue Fahey of Memorial University of Newfoundland and Renée deGannes, Canadian Dental Association, Ottawa, who have taken over the reins and accepted a two-year term as EBLIG’s new co-conveners. EBLIG has been active this past year, mainly in the area of conference workshops and sessions. At the organizers’ invitation, we successfully submitted and presented a half-day post-conference workshop for the 4th International Evidence Based Librarianship Conference. How to Assess the Evidence: A Critical Appraisal Tool for Library and Information Research, facilitated by Lindsay Glynn, of Memorial University of Newfoundland, was an extremely well-received session with nearly 30 participants in attendance. Virginia Wilson and Stephanie Hall created a practical toolkit on evidence-based library and information practice specifically for public librarians: http://ebltoolkit.pbwiki.com/. This toolkit was unveiled at the 2007 CLA conference in St. John’s by Stephanie Hall. A wiki was launched in 2007 entitled eblibrarianship: the Wiki of the Evidence Based Librarianship Interest Group (EBLIG) to facilitate interest group collaboration, sharing, and information access, as well as a means of communication: http://eblibrarianship.pbwiki.com/ EBLIG members are active in the EBLIP community and beyond: EBLIG members edited and contributed to an EBL-themed issue of Feliciter. EBLIG members are participating as editors, on the editorial advisory board, and on the evidence summary team of the open access journal, Evidence Based Library and Information Practice. As a result of a posting on the eblibrarianship wiki for the CLA conference session, Evidence-Based Librarianship: A Toolkit for Public Libraries, Virginia Wilson and Stephanie Hall have been invited to present a 1-hour audio conference for the Education Institute in the fall of 2007. International membership to the interest group is available without having to become a full member of CLA. For only $30 CAD, people residing outside of Canada can join EBLIG and take advantage of networking and continuing education opportunities. More information on international membership is available at http://www.cla.ca/about/igroups/evidence_based.htm. The past two years have been challenging and exciting. An active and engaged membership goes a long way in making this interest group vital and productive.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.001 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.329 | 0.235 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".